Familiarising Probabilistic Distance Clustering System of Evolving Awale Player

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In this paper, the authors developed a new technique based on Probabilistic Distance Clustering (PDC) for evolving Awale player and to compare its performance with that of a technique based on approximation of minimum and maximum operators with generalized mean-value operator. The basic theory of pd-clustering is based on the assumption that the probability of an Euclidean point belonging to a cluster is inversely proportional to its distance from the cluster centroid. Treating game strategies as a vector space model, it is possible to extend pd-clustering technique to game playing by estimating the probability that a given strategy is in a certain cluster of game strategies.
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